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Machine learning-assisted multiphysics coupling performance optimization in a photocatalytic hydrogen production system

机译：电机学习辅助多发性耦合性能优化在光催化氢气生产系统中

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摘要

The application of photocatalytic hydrogen evolution is plagued by low energy conversion efficiency. To achieve the maximum hydrogen production, a new optimization model with partial differential equations (PDE) as constrained conditions is constructed, and the Sobol' method is employed to quantify the operation condition prioritization and determine the decision variables. To alleviate the computation efforts of daunting optimization problem, the Gaussian process regression (GPR) method is developed to approximate the original optimization problem. A three-dimension multiphysical coupling mathematical model for a photocatalytic reactor is built and validated by the experimental data, which is further employed to investigate the dynamic evolution behaviors of hydrogen yield under different working conditions and compute training samples. A new memetic algorithm integrating the performance advantages of whale optimization (WO) method and simulated annealing (SA) algorithm is developed to search for a high-quality solution. The encouraging results on typical reaction processes clearly imply that the proposed method achieves the coordination of reaction mechanism and operation parameters, reduces the computing complex and load, and successfully finds the optimal operation conditions that will maximize the conversion efficiency of solar energy to hydrogen energy. The research outcomes open new avenues to improve the hydrogen production and accelerate industrial applications of the technology.
机译：光催化氢进化的施加通过低能量转换效率困扰。为了实现最大氢气产生，构造了具有部分微分方程（PDE）的新优化模型作为约束条件，并且使用Sobol'方法来量化操作条件优先级并确定决策变量。为了减轻令人生畏的优化问题的计算工作，开发了高斯进程回归（GPR）方法以近似原始优化问题。通过实验数据建立和验证了光催化反应器的三维多体耦合数学模型，进一步用于研究不同工作条件下的氢产量的动态演化行为和计算训练样品。开发了一种新的膜算法，其集成了鲸鲸优化（WO）方法和模拟退火（SA）算法的优点，以搜索高质量的解决方案。令人鼓舞的结果对典型反应过程明确暗示，该方法达到了反应机理和操作参数的协调，减少了计算的复杂和负载，并成功地找到了最大化太阳能转换效率对氢能的最佳运行条件。研究成果开辟了新的途径，以改善氢生产并加速技术的工业应用。

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